Interferometric Signal Processing

ABSTRACT

A method of error handling in a slope monitoring system that generates slope movement data from interferometric signal processing of radar images of the slope monitoring system. The error handling occurs in two steps. The movement data is corrected for changes in atmospheric conditions and disturbances are identified. It is convenient to mask the regions identified as disturbed in the display of the corrected movement data. Typical disturbances include short term disturbances, such as trucks, and long term disturbances, such as vegetation.

BACKGROUND TO THE INVENTION

The Slope Stability Radar (SSR) is a ground based interferometric radar,designed to detect the precursor movements to slope failure in open cutmines. It achieves this by remotely monitoring the movement of the rockface, and using these movements to predict slope failure. The monitoredregion is scanned regularly, producing a close to real time assessmentof the rock face and allows mine staff to react quickly to changes inthe rock face. To achieve maximum safety benefit high accuracy andreliable measurements are required.

A mine using the SSR is reliant upon the measurement data from thesystem for safety warnings. Any disturbance to the signal could, unlessidentified, be incorrectly interpreted as a wall movement. Haul trucksand other mining vehicles can block the path between the SSR and targetsection of wall. Mine plant equipment, such as pumps and lighting plantsmay be parked in regions of the image. Grass and other vegetation mayalso be growing on the face in the field of view of the radar. Even theair itself between the SSR and the wall can appear to induce movementswhen there is a change in atmospheric pressure, humidity or temperature.This affects the refractive index of the air and thus the speed of theradar wave through the medium.

All these effects reduce the precision of the measurements. The regionaldisturbances of the haul trucks often disguise the true movements inthose areas. Vegetation causes random fluctuations in the signal, whichcould be interpreted as movement. If the reduced measurement precisionof some areas is left unidentified, the user's confidence for other morestable areas on the wall will be reduced. Finally atmospheric changesappear to produce global movements of the whole wall which, if leftuncompensated, would appear as fluctuating movements of the wall, andnot allow the true movements to be detected. An even worse phenomenonfor atmospherics is that they can also produce a permanent change to themeasured displacement due to signal ambiguity issues resulting from themeasurement method. The result of these effects can be a lack of trustby the user in the measurements made by the SSR. To regain the trust ofthe user these effects need to be corrected or removed, or at leastidentified and displayed to the user.

To allow understanding and identification of these disturbances it isimportant to understand the method of operation of the SSR. This hasbeen described previously in our granted U.S. Pat. No. 6,850,183. TheSSR uses the phase of the returned signal to determine the movement of awall slope. As explained in Reeves B. A. et al., “Developments inMonitoring Mine Slope Stability using Radar Interferometry”; IEEEPublication 0-7803-6359-0/00, pp. 2325-2327 and Bamler R. et al.,“Synthetic aperture radar interferometry”, Inverse Problems. Vol 14, pp1-54 1998, phase change can be converted to displacement using thefollowing formula:

Δd=Δøλ/4π+nλ/2  [1]

where Δd is the displacement, Δø is the measured change in phase, λ isthe wavelength of the carrier frequency of the radar (32 mm) and n is aninteger unknown.

The parameter “n” corresponds to the number of wavelength cycles thetarget has moved between scans. For small time intervals this is assumedto be 0. As the phase is a number between +/−π, the result is that forthe measured distance change to be correct the actual distance changeneeds to be less than +/−λ/4 or +/−8 mm. As a result, processing methodsneed to be both robust for removal of disturbances to the phase change(and thus displacement) measurement, as well as being able to improvethe accuracy of the estimation of the unknown “n” within thedisplacement calculation formula.

One of the processing techniques described in U.S. Pat. No. 6,850,183 toimprove signal quality was atmospheric correction. In this technique, asingle reference section of wall was used to determine the atmosphericeffect on the signal. This calculated correction was then applied to theremainder of the wall. Simple processing based on changes in amplitudeand phase for detection of vegetation and other spurious signals, suchas trucks, was also discussed.

The problems with the known technique for disturbance rejection include:

-   -   Large atmospheric changes in the environment may induce a        wrapping error when the difference between subsequent        measurements is greater than +/−λ/4 mm;    -   As the range to a single atmospheric correction region increases        the chance of getting a wrapping error increases, thus limiting        the range of the system;    -   A wall movement in the atmospheric correction region will        produce a false apparent movement of the wall;    -   A truck, vegetation or other type of disturbance in the        atmospheric region will produce a false apparent movement of the        wall;    -   The single atmospheric region correction works best for targets        at a similar range. If the targets of interest are distributed        over a large band of ranges, the atmospheric correction does not        work effectively;    -   The known approaches do not compensate for temperature changes        or other effects on the radar electronics;    -   Disturbing signals from trucks and other mining vehicles can        cause step changes in the displacement measurements, confusing        the user and making automatic alarming difficult; and    -   Vegetation on the rock face reduces the accuracy of the        displacement measurements for that location, confusing the user        and reducing confidence in the rest of the measurements.

Identifying and rejecting these disturbances will significantly improvethe precision of the SSR, increase confidence in the measurementaccuracy, and reduce the number of false positive alarms. This will inturn improve the acceptance of the SSR technology leading to improvedmine safety.

DISCLOSURE OF THE INVENTION

In one form, although it need not be the only or indeed the broadestform, the invention resides in an anomaly detection and correctionmodule for a slope monitoring system comprising:

an atmospheric correction module that corrects slope movementmeasurements for anomalies caused by atmospheric changes; anda disturbance detection module that identifies disturbances that causeerrors in the slope movement measurements.

The disturbance detection module suitably masks regions affected by theerrors.

In another form the invention resides in a method of error handling ininterferometric signal processing for a slope monitoring systemincluding the steps of:

extracting uncorrected movement data from interferometric radarmeasurements;correcting the movement data for changes in atmospheric conditions;identifying disturbances in the corrected movement data; anddisplaying the corrected movement data and regions affected by thedisturbances.

Suitably the regions affected by the disturbances are masked.

Disturbances are suitably blocking disturbances caused by short-termblockage of the radar signal, for example by trucks and other equipment,and random disturbances caused by long-term interference, for example byvegetation.

Atmospheric correction is suitably achieved by estimating the change inthe signal speed due to changes in the refractive index of the air andthe offset induced at zero range.

Identifying disturbances in the movement data is suitably achieved bydetecting variations in short-term and long-term signal coherence.

In a further form the invention resides in a method of atmosphericcorrection of movement data comprising multiple data points in a slopemonitoring system including the steps of:

selecting a plurality of atmospheric correction regions at differentranges;deducing displacement data within the selected regions;determining a search space;calculating a cost function for a grid of points within the searchspace;using a minimisation algorithm to determine a correction slope andoffset; andapplying the correction slope and offset to the movement data.

Suitably the step of a using a minimisation algorithm includescalculating local minima within the search space and using the localminima to seed a multidimensional minimisation algorithm to find trueminima.

In a yet further form the invention resides in a method of identifyingdisturbances of movement data comprising multiple data points in a slopemonitoring system including the steps of:

determining short-term coherence for each data point;averaging the short-term coherence to determine long-term coherence;comparing the long-term coherence to a first threshold and masking thedata point if the long-term coherence is less than the first threshold;andcomparing a ratio of the short-term coherence to the long-term coherencewith a second threshold and masking the data point if the ratio is lessthan the second threshold.

The method may further include the step of comparing signal amplitude ofthe data point with a sky threshold and classifying the data point assky if the amplitude is less than the sky threshold.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a block schematic of the data processing steps within aSlope Stability Radar or similar monitoring system;

FIG. 2 is a flow diagram demonstrating the steps involved in determiningthe atmospheric correction to apply to radar measurements;

FIG. 3 shows an example of selecting a number of stable regions on theface to use for atmospheric correction;

FIG. 4 shows the range dependence of the phase change with respect tothe atmospheric correction required, in conjunction to the problemsassociated with ambiguity wrapping;

FIG. 5 shows the combined use of the wrapped line fit algorithm incombination with weighting of the cost function depending on thesimilarity to a previous line of best fit;

FIG. 6 shows a flow diagram demonstrating the techniques for removingthe blocking effect of trucks and identifying the disturbance fromvegetation from the actual movement of the wall;

FIG. 7 shows typical normal signals when monitoring wall movements,ranging from small movements to rapid movements;

FIG. 8 shows typical signals due to blocking due to trucks anddisturbance due to vegetation; and

FIG. 9 shows a typical display showing the blocking and disturbanceregions in conjunction to the normal displacement measurements.

DETAILED DESCRIPTION OF THE DRAWINGS

FIG. 1 displays the data flow for a SSR (slope stability radar) unit101. Radar data from the radar module 102, in the form of a number ofscans of the rock face, as well as video data from a video module 104and atmospheric data from external weather sensing module 103 are fedinto the system. The interferometric signal processing unit 105 consistsof a number of modules, including peak detection and deformationcalculation module 106, anomaly detection and correction module 112which includes atmospheric correction module 107 and disturbancedetection module 108, and signal tracking and unwrapping module 109.After the interferometric processing the data is combined with the videosignal 110 for image correlation and further processing. Finally theresultant data is passed to the display and failure prediction module111.

Signals from the radar module 102 are processed in the peak detectionand deformation module 106 to convert them from radar range profile tophase and amplitude changes for the strongest signals for thatfootprint. From this the displacement data can be extracted. This datais then passed to the anomaly detection and correction module 112. Thepurpose of this module is to either eliminate, reduce or at least detectanomalies in the data caused by external effects on the system. Thesedisturbances include changes in the atmosphere, mine vehicles such astrucks as well as vegetation on the wall face. Without this modulesignificant false movement can appear in the deformation images producedby the SSR unit, leading to a false assumption that the wall is movingor the obscuration of actual wall movements. Both are failures of thesystem in the eyes of the user. Module 112 uses the displacement dataand signal amplitudes to determine if the deformation is a true measureof the wall movement. It does this via two steps; firstly the effect ofthe atmosphere is identified and corrected in the atmospheric correctionmodule 107. Without atmospheric correction, fluctuations of up to 30 mmcan occur in the deformation images. Considering a typical systemmeasurement accuracy is less than 1 mm, this error is completelyunacceptable. Secondly, more localised disturbances are identified andeliminated from the image in the disturbance detection module 108.Without this trucks can temporarily disturb the signal, inducing jumpsin the deformation data, which are difficult to identify in comparisonto stepped wall movements. The result of module 112 is a clean signal,free from external disturbances allowing the user to see when true wallmovements occur.

FIG. 2 shows a flow diagram for determining the atmospheric correctionparameters and applying that correction to the radar measurementscalculated by the Peak Detection and Deformation Calculation module 106.Atmospheric correction is suitably achieved by estimating twoparameters, the ppm (parts per million) change in the signal speed dueto changes in the refractive index of the air and the offset induced bythe radar electronics or other effects at zero range.

The inventors have found that in many cases the effect of offset is lessimportant so the offset can be constrained to zero. Another approach isto determine the offset separately, for example by measuring the radarreflection from the feedhorn, thereby eliminating influences other thanthe radar electronics.

Firstly, the displacements within the selected atmospheric regions (seeFIG. 3) are deduced 201, producing a plot similar to FIG. 4. A searchspace is then defined 202 based on the previous slope and offset(initially set to zero for each on start-up), which is then tessellatedinto a number of grid points. The weighted cost function is thenevaluated 203 at each of these grid points, to determine a point closeto the minima 204. These “grid” minima are then used to seed amultidimensional minimisation algorithm 205 to determine the exactminima 206. This calculated offset and slope is then used to correct theremainder of the displacement image 207.

FIG. 3 shows a typical selection of atmospheric regions to allowcorrection of atmospheric changes in the air between the radar and thewall. The display of FIG. 3 is a greyscale rendering of the usual colourdisplay. The region 301 is a region of interest that is moving or isexpected to move. A number of regions 302 (separate from the area movingor expected to move 301) are selected at various ranges A1, A3, or asingle long region A2 could be used. The regions 302 should be within astable region of the wall. These areas are often determined by the localgeo-technical engineer via their understanding of the nature of the rockface. The goal of selecting a number of atmospheric regions is to obtaina measure of the atmospheric correction required from a number of pointsat different ranges.

The requirement to have an indication of the atmospheric region at anumber of different ranges is to allow the slope and offset to beestimated at a number of ranges. This is because an estimate at closerange will be less accurate than an estimate at a far range, but is alsoless likely to have the wrapping issue discussed below with reference toFIG. 4.

FIG. 4 displays a graphical representation of the estimation or wrappingproblem. Points 405 are measured for various displacements within theatmospheric regions for a given interval of time. The points 405 aredisplayed on the graph with respect to the range 408 to the point. Forpoints at close range, the points are unaffected by wrapping issues,however as the range to the atmospheric correction region increases apoint is reached where the correction is greater than the systemambiguity (+/−λ/4 or 8 mm) 402. This is a consequence of the integervalue “n” in equation 1. To estimate the slope 404 and offset 403, aline is fitted to the atmospheric correction points. To mirror thewrapping issues with the data points, the line 406 is also wrapped 407.FIG. 4 shows a number of points 405 fitted to the unwrapped line 406 andwrapping to the wrapped version of the line 407. Once the slope andoffset is determined this is used to correct the remainder of thedeformation image, where the slope corresponds to the ppm change in thespeed of the electromagnetic signal from the radar and the offsetcorresponds to any phase drift in the radar electronics (normallyinduced by temperature changes).

One process for estimating a line which best fits the given data pointscan be understood by reference to FIG. 5. A cost function is utilisedwhich is equal to the root mean square estimate of the distance betweenthe actual data points 503 and the wrapped version of the line. Aminimisation algorithm is then used to test a number of different slopesand offsets to find the one that best fits the data points.

Another issue that must to be managed within the algorithm is to ensurethat the slope and offset estimate stays within given limits. If thereis no limit placed on the slope it is possible that the wrapping couldincrease to a point where there may be a solution where a highly wrappedline will fit through almost all of the points. This slope however isnot a correct estimate of the ppm change due to atmospherics. To limitthe offset and slope, an initial estimate 501 of the offset and slope isused. This can be determined via the previous offset and slope, or couldbe calculated from atmospheric measurements from external weathersensing module 103. This estimate is firstly used to limit the searchspace. Secondly, it is used to calculate a cost function weighting 502.Atmospheric measurements can also be used to correct long term errorsdue to movement of the atmospheric region.

As well as changes in the atmosphere between the radar and the target,other external disturbances can occur. These can be classified into twotypes, blocking disturbances and random disturbances. Objects that blockthe radar beam for a short period of time cause blocking anomalies. Inthe mining context, these include haul trucks, other mining vehicles andmine personnel. Objects that induce a continuous disturbance to the beamcause random disturbance. They include vegetation on the face such asgrass and trees, as well as permanent vibrating equipment such as waterpumps and lighting plants. A random disturbance is also a very rapidlymoving wall, where the rock face is throwing rocks or material as itaccelerates to failure.

A measure which is key to the identification and separation of thesedifferent anomalies is the interferometric coherence (or complexcorrelation coefficient). This is defined by Bamler (referenced above)as:

γ=E[u ₁ u ₂*]/√(E[|u ₁|² ]E[|u ₂|²])  [2]

where E is the expected value, u₁ is the complex radar signal at time 1,u₂ is the complex radar signal at time 2 and γ is the resultantinterferometric coherence. The interferometric coherence is between 0and 1 where 1 is high coherence and 0 is low coherence.

This is calculated for a zone around the signal peak for each returnedecho from the wall. Identification of regions that contain blockingdisturbances and random disturbances allows these regions to behighlighted as unreliable and disregarded for alarm purposes. As will beevident by reference to FIG. 9, it is also important to identify the skyand to apply a mask to ignore the sky.

FIG. 6 shows a flow diagram for classification of sky, blockingdisturbances (trucks) and random disturbances (vegetation). The processfollows from the atmospheric correction described by FIG. 2. Initiallythe short-term 601 and long-term 602 signal coherence is determined foreach point on the wall. This is then fed into a decision matrix 603. Ifthe signal is of low amplitude in comparison to the user defined skymask threshold 610, it is classified as sky 607. Next the long-termcoherence is compared 604 to a disturbance mask threshold 611. If thelong-term coherence is less than this level, the point is masked asdisturbed. Finally, the ratio of the short-term coherence to thelong-term coherence is calculated and compared 605 to a given blockingmask threshold 612. If the calculated ratio is less then the blockingmask threshold the point is classified as a blocking mask 609. If thepoint passes all these tests, it is displayed in the usual manner 606.

It is convenient to use hysteresis in the threshold calculation for allof these decisions, ensuring a point close to the threshold point willnot flicker between masked and not masked. This improves the user'sconfidence in the masking decision.

FIG. 7 and FIG. 8 show typical signal response graphs for a number ofdifferent conditions. These graphs are an example of the response of theradar for a single point within an area of interest.

FIG. 7 a displays the typical response for a slow moving wall 701. Forthis signal, the signal coherence 702 is reasonably constant, beingclose to the ideal value of 1.0 and the displacement 703 increasessteadily over time. In contrast, FIG. 7 b shows a wall 704 that ismoving more rapidly and demonstrates acceleration 706. The coherence 705starts to decay, reducing from close to 1.0 when the rate of movement issmall to around 0.5 as the wall speeds up. For a rapidly moving wall anyinstantaneous estimate of the coherence is quite variable, so to allowdecisions to be made more reliably, a second measure is used, thelong-term coherence. This is a smoothed version of the estimate producedby equation [2]. In FIG. 7 and FIG. 8, both long term and short-termcoherence are graphed.

FIG. 8 shows the typical responses for both trucks and vegetation.Trucks 801 produce spikes in the coherence plot 802, with a cleardifference being noted between the short-term and long-term coherence.Often there is also a displacement 803 associated with these events.Vegetation 804 appears like a permanent disturbance 805 to the coherencesignal and an erratic displacement signal 806. As with the rapidlymoving wall, the long-term coherence is the best determinant of thisanomaly.

In FIG. 9, a greyscale display of a typical deformation image is shown.Within the scan region there is normal movement, as well as a number ofmasks. The sky mask is displayed as black, the disturbance mask 902 isshown as green on a colour display. The blocking mask 901 is shown asgrey in a colour display. In the image they are shown as solid colours,but alternatively they could be a shaded version of the normaldisplacement colour for that area on the wall, thus allowing the pointto be both identified and the movement displayed. This is especiallyimportant for the disturbance mask 902 as this can be induced by arapidly moving wall. Other representations could include bordering ofthe pixel with a given colour. All display methods are focussed onallowing the users to identify the point, thus warning them ofmeasurement issues for those areas on the wall, thus retaining theirconfidence in the accuracy of the measurements for the remainder of thewall.

The methods of interferometric signal processing correct for anomaliesand disturbances in the measured displacement data caused by atmosphericvariation, blocking disturbances such as heavy equipment and randomdisturbances such as vegetation. These corrections enhance the precisionof the displacement data leading to more reliable alarms and hencegreater user confidence. Throughout the specification the aim has beento describe the preferred embodiments of the invention without limitingthe invention to any one embodiment or specific collection of features.

1. A method of error handling in interferometric signal processing for aslope monitoring system including the steps of: extracting uncorrectedmovement data from interferometric radar measurements; correcting themovement data for changes in atmospheric conditions; identifyingdisturbances in the corrected movement data; and displaying thecorrected movement data and regions affected by the disturbances.
 2. Themethod of claim 1 wherein displaying regions affected by thedisturbances includes masking the regions.
 3. The method of claim 1wherein the disturbances are blocking disturbances caused by short-termblockage of the radar signal,
 4. The method of claim 3 wherein thedisturbances are caused by trucks.
 5. The method of claim 1 wherein thedisturbances are random disturbances caused by long-term interference.6. The method of claim 5 wherein the disturbances are caused byvegetation.
 7. The method of claim 1 wherein the step of correcting themovement data for changes in atmospheric conditions is achieved byestimating the change in the signal speed due to changes in therefractive index of the air and the offset induced at zero range.
 8. Themethod of claim 1 wherein disturbances are identified in the movementdata by detecting variations in short-term and long-term signalcoherence.
 9. The method of claim 8 further including the step ofcomparing the long-term coherence of a data point to a disturbance maskthreshold and classifying the data point as disturbed if the long-termcoherence is less than the disturbance mask threshold.
 10. The method ofclaim 8 further including the step of comparing a ratio of short-termcoherence to long-term coherence of a data point to a blocking maskthreshold and classifying the data point as blocking mask if the ratiois less than the blocking mask threshold.
 11. An anomaly detection andcorrection module for a slope monitoring system comprising: anatmospheric correction module that corrects slope movement measurementsfor anomalies caused by atmospheric changes; and a disturbance detectionmodule that identifies disturbances that cause errors in the slopemovement measurements.
 12. The anomaly detection and correction moduleof claim 11 wherein the disturbance detection module masks regionsaffected by the errors.
 13. The anomaly detection and correction moduleof claim 11 further comprising input to the atmospheric correctionmodule from a weather sensing module.
 14. A method of atmosphericcorrection of movement data comprising multiple data points in a slopemonitoring system including the steps of: selecting a plurality ofatmospheric correction regions at different ranges; deducingdisplacement data within the selected regions; determining a searchspace; calculating a cost function for a grid of points within thesearch space; using a minimisation algorithm to determine a correctionslope and offset; and applying the correction slope and offset to themovement data.
 15. The method of claim 14 wherein the step of a using aminimisation algorithm includes calculating local minima within thesearch space and using the local minima to seed a multidimensionalminimisation algorithm to find true minima.
 16. A method of identifyingdisturbances of movement data comprising multiple data points in a slopemonitoring system including the steps of: determining short-termcoherence for each data point; averaging the short-term coherence todetermine long-term coherence; comparing the long-term coherence to afirst threshold and masking the data point if the long-term coherence isless than the first threshold; and comparing a ratio of the short-termcoherence to the long-term coherence with a second threshold and maskingthe data point if the ratio is less than the second threshold.
 17. Themethod of claim 16 further including the step of comparing signalamplitude of the data point with a sky threshold and classifying thedata point as sky if the amplitude is less than the sky threshold.
 18. Aslope monitoring system of the type comprising at least a radar modulegenerating interferometric movement measurements of a slope furthercomprising an anomaly detection and correction module, the anomalydetection and correction module comprising: an atmospheric correctionmodule that corrects slope movement measurements for anomalies caused byatmospheric changes; and a disturbance detection module that identifiesdisturbances that cause errors in the slope movement measurements. 19.The slope monitoring system of claim 18 comprising a video module thatprovides visual images of the slope.